{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 半监督学习"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split \n",
    "import numpy as np\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计时器\n",
    "def timer (func):\n",
    "    def wrapper(*args,**kwargs): \n",
    "        start = time.time()\n",
    "        result = func(*args,**kwargs)\n",
    "        end = time.time()\n",
    "        print(func.__name__+'运行时间：',end-start)\n",
    "        return result\n",
    "    return wrapper\n",
    "\n",
    "@timer\n",
    "def load_train():\n",
    "    # 导入数据\n",
    "    y_train_df = pd.read_csv('../../preprocess_data/train_y_33465.csv',usecols=['label'])\n",
    "    x_train_df = joblib.load('./x_train.lz4')\n",
    "    x_train_df = x_train_df.fillna(-1)\n",
    "\n",
    "    x_train_arr = x_train_df.values\n",
    "    y_train_arr = y_train_df.values.ravel()\n",
    "    return x_train_arr,y_train_arr\n",
    "\n",
    "@timer\n",
    "def load_unlabel():\n",
    "    prob_unlabel_df = pd.read_csv('../predict_unlabel/preds/unlabel_pred.csv',usecols=['prob'])\n",
    "    x_unlabel_df = joblib.load('./x_unlabel.lz4')\n",
    "    unlabel_df = pd.concat([x_unlabel_df,prob_unlabel_df],axis=1)\n",
    "    unlabel_df = unlabel_df.sort_values(by=['prob'],ascending=False)\n",
    "    unlabel_df = unlabel_df.reset_index(drop=True)\n",
    "    unlabel_df = unlabel_df.fillna(-1)\n",
    "\n",
    "\n",
    "    y_unlabel_df = unlabel_df[['prob']].copy()\n",
    "    x_unlabel_df = unlabel_df.drop(columns=['prob'])\n",
    "\n",
    "    y_unlabel_df.loc[:int(0.1*len(y_unlabel_df)),'prob'] = 1\n",
    "    y_unlabel_df.loc[int(0.1*len(y_unlabel_df)):,'prob'] = 0\n",
    "    # 按prob 由高到低排列\n",
    "    x_unlabel_arr = x_unlabel_df.values\n",
    "    y_unlabel_arr = y_unlabel_df.values.ravel()\n",
    "\n",
    "\n",
    "    # 颠倒一下正负样本的先后次序\n",
    "    x_unlabel_0 = x_unlabel_arr[int(0.1*len(x_unlabel_df)):]\n",
    "    x_unlabel_1 = x_unlabel_arr[:int(0.1*len(x_unlabel_df))]\n",
    "    x_unlabel_arr = np.vstack((x_unlabel_0,x_unlabel_1))\n",
    "\n",
    "    y_unlabel_0 = y_unlabel_arr[int(0.1*len(y_unlabel_df)):]\n",
    "    y_unlabel_1 = y_unlabel_arr[:int(0.1*len(y_unlabel_df))]\n",
    "    y_unlabel_arr = np.hstack((y_unlabel_0,y_unlabel_1))\n",
    "    \n",
    "    return x_unlabel_arr,y_unlabel_arr,-int(0.1*len(x_unlabel_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load_train运行时间： 7.0759968757629395\n",
      "load_unlabel运行时间： 27.868000984191895\n"
     ]
    }
   ],
   "source": [
    "x_train_arr,y_train_arr = load_train()\n",
    "x_unlabel_arr,y_unlabel_arr,start = load_unlabel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "START = start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 半监督相关函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import xgboost as xgb\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "import lightgbm as lgb\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# @timer\n",
    "def gen_semiset(x_unlabel_arr,y_unlabel_arr,start=-6653,num=10,ratio=0.1):\n",
    "    num_0 = num-int(num*ratio)\n",
    "    num_1 = int(num*ratio)\n",
    "    tmp = x_unlabel_arr[start-num_0:start+num_1]\n",
    "    y_tmp = y_unlabel_arr[start-num_0:start+num_1]\n",
    "    return tmp,y_tmp\n",
    "\n",
    "# @timer\n",
    "def combine_data(x_train_arr,y_train_arr,tmp,y_tmp):\n",
    "\n",
    "\n",
    "    # 容易出错vstack hstack\n",
    "    x_train_tmp = np.vstack((x_train_arr,tmp))\n",
    "    y_train_tmp = np.hstack((y_train_arr,y_tmp))\n",
    "    return x_train_tmp,y_train_tmp\n",
    "\n",
    "# @timer\n",
    "def gen_local_train(x_unlabel_arr,y_unlabel_arr,x_train_arr,y_train_arr,\n",
    "                    start=-6653,num=10,ratio=0.1):\n",
    "    tmp,y_tmp = gen_semiset(x_unlabel_arr,y_unlabel_arr,start,num,ratio)\n",
    "    return combine_data(x_train_arr,y_train_arr,tmp,y_tmp)\n",
    "\n",
    "# @timer\n",
    "def stratify_split(x_train_arr,y_train_arr,random_state=3096,test_size=0.3):\n",
    "    # 层次划分出local test \n",
    "    \n",
    "    x_train_new, x_test_new, y_train_new, y_test_new = train_test_split(x_train_arr,\n",
    "                                                                        y_train_arr,\n",
    "                                                                        random_state=random_state,\n",
    "                                                                        test_size=test_size,\n",
    "                                                                        stratify=y_train_arr)\n",
    "    return  x_train_new, x_test_new, y_train_new, y_test_new\n",
    "\n",
    "\n",
    "# @timer\n",
    "def train(x_train,y_train,x_test,y_test,model_name='rfc'):\n",
    "    #2.建立模型 \n",
    "#     model_names = ['rfc','gbc','xgb','lgb','dtc']\n",
    "    if model_name == 'rfc':\n",
    "        # RandomForestClassifier\n",
    "        \n",
    "        rfc  =RandomForestClassifier(n_estimators=10,\n",
    "                                     n_jobs =32,\n",
    "                                     max_features='sqrt',\n",
    "                                     class_weight='balanced',\n",
    "#                                      verbose =1,\n",
    "                                     random_state=2018)\n",
    "    elif model_name == 'gbc':\n",
    "        \n",
    "        gbc = GradientBoostingClassifier(loss='deviance',learning_rate =0.1,n_estimators=300,subsample=0.9,max_depth=3,verbose=1,random_state=2018)\n",
    "    elif model_name == 'xgb':\n",
    "        \n",
    "        params={\n",
    "        'booster':'gbtree',\n",
    "        'objective': 'binary:logistic',\n",
    "        'early_stopping_rounds':100,\n",
    "        'scale_pos_weight': float(len(data_label)-np.sum(data_label.values))/float(np.sum(data_label.values)),  # 负例样本除以正例样本\n",
    "        'eval_metric': 'auc',\n",
    "        'gamma':1,\n",
    "        'max_depth':6,\n",
    "        'lambda':1,\n",
    "        'subsample':0.9,\n",
    "        'colsample_bytree':0.9,\n",
    "        'min_child_weight':1, \n",
    "        'eta': 0.04,\n",
    "        'seed':2010,\n",
    "        'nthread':32\n",
    "            }\n",
    "    elif model_name == 'lgb':\n",
    "        \n",
    "        parameters = {\n",
    "            'boost':'gbdt',\n",
    "            'num_leaves':135, \n",
    "            'scale_pos_weight':float(len(y_train)-np.sum(y_train.ravel()))/float(np.sum(y_train.ravel())),\n",
    "            'max_depth':-1,\n",
    "            'learning_rate':.05,\n",
    "            'max_bin':200,\n",
    "            'min_data_in_leaf' : 60,\n",
    "            'objective':'binary',\n",
    "            'metric':'auc',\n",
    "            'verbose':1,\n",
    "            'num_threads':36\n",
    "        }\n",
    "    elif model_name == 'dtc':\n",
    "        dtc = DecisionTreeClassifier(class_weight='balanced')\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "    #3.训练模型\n",
    "#     print('开始训练！')\n",
    "    import joblib\n",
    "    if model_name == 'rfc':\n",
    "        rfc.fit(x_train,y_train)\n",
    "    #     joblib.dump(rfc,'./model/rfc_model')\n",
    "    elif model_name == 'gbc':\n",
    "        gbc.fit(x_train,y_train)\n",
    "    #     joblib.dump(gbc,'./model/rgbc_model')\n",
    "    elif model_name =='xgb':\n",
    "        dtrain = xgb.DMatrix(x_train,y_train)\n",
    "        dvalid = xgb.DMatrix(x_test,y_test)\n",
    "        dtest = xgb.DMatrix(x_test)\n",
    "        watchlist  = [(dvalid,'valid')]\n",
    "        xgb_model = xgb.train(params,dtrain,num_boost_round=300,evals=watchlist)\n",
    "    #     xgb_model.save_model('./model/xgb_model')\n",
    "    elif model_name == 'lgb':\n",
    "        lgb_train = lgb.Dataset(x_train, y_train.ravel())\n",
    "        lgb_model = lgb.train(parameters,lgb_train,num_boost_round=50)\n",
    "    #     lgb_model.save_model('./model/lgb_model')\n",
    "    elif model_name == 'dtc':\n",
    "        dtc.fit(x_train,y_train)\n",
    "    else:\n",
    "        pass\n",
    "    # lg.fit(x_train,y_train.ravel())\n",
    "    # lsvm.fit(x_train,y_train)\n",
    "    # history = model.fit(x_train,y_train,epochs=2,batch_size=1024,class_weight = 'auto',validation_data=(x_test,y_test))\n",
    "     #4.预测结果\n",
    "#     print('开始预测！')\n",
    "    if model_name=='rfc':\n",
    "        y_pre = rfc.predict_proba(x_test)[:,1]\n",
    "    elif model_name=='gbc':\n",
    "        y_pre = gbc.predict_proba(x_test)[:,1]\n",
    "    elif model_name=='xgb':\n",
    "        dtest = xgb.DMatrix(x_test)\n",
    "        y_pre = xgb_model.predict(dtest).ravel()\n",
    "    elif model_name=='lgb':\n",
    "        y_pre = lgb_model.predict(x_test)\n",
    "    elif model_name == 'dtc':\n",
    "        y_pre = dtc.predict(x_test)[:,1]\n",
    "    else:\n",
    "        pass\n",
    "    \n",
    "    auc = metrics.roc_auc_score(y_test, y_pre)\n",
    "#     print('AUC:',auc)\n",
    "    return auc\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_new, x_test_new, y_train_new, y_test_new = stratify_split(x_train_arr,y_train_arr,random_state=2018,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def progress(percent,width=50):\n",
    "    '''进度打印功能'''\n",
    "    if percent >= 100:\n",
    "        percent=100\n",
    "  \n",
    "    show_str='[%-*s]' %(width,int(width * percent/100)*\">\") #字符串拼接的嵌套使用\n",
    "    print('\\r%s %d%%' %(show_str,percent),end='') # \\r 回到行首，覆盖之前的\n",
    "\n",
    "@timer\n",
    "def semi_train(iter=3,start=-6653,step=10,length=40,early_stop=120):\n",
    "    print('第{}轮：'.format(iter))\n",
    "    auc_list = []\n",
    "    num_list = []\n",
    "    early_tmp = 0\n",
    "    for num in range(0,length,step):\n",
    "        # ratio取值范围：1/16 ~1/10\n",
    "        x_train_tmp,y_train_tmp = gen_local_train(x_unlabel_arr,y_unlabel_arr,x_train_new,y_train_new,start,num,1/step)\n",
    "        auc = train(x_train_tmp,y_train_tmp,x_test_new,y_test_new,model_name='rfc')\n",
    "        auc_list.append(auc)\n",
    "        num_list.append(num)\n",
    "        # early_stopping 模块\n",
    "        if len(auc_list)<=1:\n",
    "            pass\n",
    "        \n",
    "        if (len(auc_list)>=2) and (auc_list[-1]<auc_list[-2]):\n",
    "            early_tmp += 1\n",
    "        else:\n",
    "            if early_tmp <=0 :\n",
    "                pass\n",
    "            else:\n",
    "                early_tmp -= 1 \n",
    "                \n",
    "        if early_tmp >= early_stop:\n",
    "            break\n",
    "        progress(int(num/length*100))\n",
    "        \n",
    "        # 分阶段保存\n",
    "        if num%int(length/20)==0:\n",
    "            log = dict(random_seed=2018,auc_list=auc_list,num_list=num_list)\n",
    "            joblib.dump(log,'./log{}'.format(iter))\n",
    "\n",
    "    log = dict(random_seed=2018,auc_list=auc_list,num_list=num_list)\n",
    "    joblib.dump(log,'./log{}'.format(iter))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第5轮：\n",
      "[>>>>>>                                            ] 13%"
     ]
    }
   ],
   "source": [
    "params = [(6,30000),(8,30000),(10,30000),(12,30000),(14,30000),(16,30000)]\n",
    "for i in range(5,-1,-1):\n",
    "    step=params[i][0]\n",
    "    length=params[i][1]\n",
    "    early_stop=int(500/step)\n",
    "    semi_train(i,START,step,length,early_stop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 4, 3, 2, 1, 0]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i for i in range(5,-1,-1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random_seed: 2018\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show('log0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 观察表现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "def show(log_path):\n",
    "    log= joblib.load(log_path)\n",
    "    print('random_seed:',log['random_seed'])\n",
    "    plt.plot(log['num_list'],pd.DataFrame(log['auc_list'],columns=['auc']).ewm(alpha=0.2).mean().values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 第二组（ratio=1/16,random_seed = 2018,step = 16）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存\n",
    "import joblib \n",
    "log2 = dict(random_seed=2018,auc_list=auc_list,num_list=num_list)\n",
    "joblib.dump(log2,'./log2')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random_seed: 2018\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show('log2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 第一组记录 (ratio=1/10,random_seed = 3096,step = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存\n",
    "import joblib \n",
    "log1 = dict(random_seed=3096,auc_list=auc_list,num_list=num_list)\n",
    "joblib.dump(log1,'./log1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random_seed: 3096\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show('log1')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: Line magic function `%his` not found.\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 线上"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### xgb bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_id = pd.read_csv('../../preprocess_data_new/valid_date.csv',usecols=['id']).values.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "dtrain = xgb.DMatrix(x, y, feature_names=list(data.columns))\n",
    "dtest = xgb.DMatrix(x_test, feature_names=list(valid.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pipeline(dtrain,dtest,test_id,iteration,random_seed,gamma,max_depth,lambd,subsample,colsample_bytree,min_child_weight):\n",
    "    if max_depth==6:\n",
    "        num_boost_round = 210\n",
    "    elif max_depth==7:\n",
    "        num_boost_round = 180\n",
    "    elif max_depth==8:\n",
    "        num_boost_round = 150\n",
    "    \n",
    "    params={\n",
    "    'booster':'gbtree',\n",
    "    'objective': 'binary:logistic',\n",
    "    'early_stopping_rounds':100,\n",
    "    'scale_pos_weight': float(len(data_label)-np.sum(data_label.values))/float(np.sum(data_label.values)),  # 负例样本除以正例样本\n",
    "    'eval_metric': 'auc',\n",
    "    'gamma':gamma,\n",
    "    'max_depth':max_depth,\n",
    "    'lambda':lambd,\n",
    "    'subsample':subsample,\n",
    "    'colsample_bytree':colsample_bytree,\n",
    "    'min_child_weight':min_child_weight, \n",
    "    'eta': 0.04,\n",
    "    'seed':random_seed,\n",
    "    'nthread':32\n",
    "        }\n",
    "    watchlist  = [(dtrain,'train')]\n",
    "    model = xgb.train(params,dtrain,num_boost_round=num_boost_round,evals=watchlist)\n",
    "    model.save_model('./xgb_models1/xgb{0}.model'.format(iteration))\n",
    "    \n",
    "    #predict test set\n",
    "    test_y = model.predict(dtest)\n",
    "    test_result = pd.DataFrame(test_id,columns=[\"id\"])\n",
    "    test_result['score'] = test_y\n",
    "    test_result.to_csv(\"./xgb_preds1/xgb{0}.csv\".format(iteration),index=None,encoding='utf-8')\n",
    "    \n",
    "    #get feature score\n",
    "    feature_score = model.get_fscore()\n",
    "    feature_score = sorted(feature_score.items(), key=lambda x:x[1],reverse=True)\n",
    "    fs = []\n",
    "    for (key,value) in feature_score:\n",
    "        fs.append(\"{0},{1}\\n\".format(key,value))\n",
    "    \n",
    "    with open('./xgb_featurescores1/feature_score_{0}.csv'.format(iteration),'w') as f:\n",
    "        f.writelines(\"feature,score\\n\")\n",
    "        f.writelines(fs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "CPU times: user 20h 16s, sys: 7min 22s, total: 20h 7min 38s\n",
      "Wall time: 42min 59s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import sys,random\n",
    "\n",
    "random_seed = list(range(1000,2000,20))\n",
    "gamma = [i/1000.0 for i in range(100,200,2)]\n",
    "max_depth = [6,7,8]\n",
    "lambd = list(range(200,400,2))\n",
    "subsample = [i/1000.0 for i in range(600,700,2)]\n",
    "colsample_bytree = [i/1000.0 for i in range(250,350,2)]\n",
    "min_child_weight = [i/1000.0 for i in range(200,300,2)]\n",
    "random.shuffle(random_seed)\n",
    "random.shuffle(gamma)\n",
    "random.shuffle(max_depth)\n",
    "random.shuffle(lambd)\n",
    "random.shuffle(subsample)\n",
    "random.shuffle(colsample_bytree)\n",
    "random.shuffle(min_child_weight)\n",
    "\n",
    "with open('params.pkl','wb') as f:\n",
    "    joblib.dump((random_seed,gamma,max_depth,lambd,subsample,colsample_bytree,min_child_weight),f)\n",
    "\n",
    "#to reproduce my result, uncomment following lines\n",
    "\"\"\"\n",
    "with open('params_for_reproducing.pkl','rb') as f:\n",
    "    random_seed,gamma,max_depth,lambd,subsample,colsample_bytree,min_child_weight = pickle.load(f)    \n",
    "\"\"\"\n",
    "\n",
    "for i in range(36):\n",
    "    pipeline(dtrain,dtest,test_id,i,random_seed[i],gamma[i],max_depth[i%3],lambd[i],subsample[i],colsample_bytree[i],min_child_weight[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def SelectModel(model_name):\n",
    "    if model_name == 'GBC':\n",
    "        from sklearn.ensemble import GradientBoostingClassifier\n",
    "        model = GradientBoostingClassifier(loss='deviance',\n",
    "                                           learning_rate =0.1,\n",
    "                                           n_estimators=300,\n",
    "                                           subsample=0.9,\n",
    "                                           max_depth=3,\n",
    "#                                            verbose=1,\n",
    "                                          random_state=2018)\n",
    "    elif model_name == 'XGB':\n",
    "        from xgboost import XGBClassifier\n",
    "\n",
    "        model = XGBClassifier(max_depth=6,\n",
    "                              learning_rate =0.04, \n",
    "                              booster='gbtree',\n",
    "                              objective='binary:logistic',\n",
    "                              early_stopping_rounds=100,\n",
    "                              scale_pos_weight=float(len(y_train)-np.sum(y_train))/float(np.sum(y_train)),\n",
    "                              eval_metric='auc',\n",
    "                              gamma=1,\n",
    "                              reg_lambda=1,\n",
    "                              subsample=0.9,\n",
    "                              min_child_weight=1,\n",
    "                              seed=2018,\n",
    "                              silent=False,\n",
    "                              n_jobs=36,\n",
    "                              num_boost_round =300\n",
    "                             )\n",
    "    elif model_name == 'RFC':\n",
    "        from sklearn.ensemble import RandomForestClassifier\n",
    "        model = RandomForestClassifier(n_estimators=1500,\n",
    "                                       n_jobs =36,\n",
    "                                       max_features='sqrt',\n",
    "                                       class_weight='balanced',\n",
    "                                       verbose =1,\n",
    "                                       random_state=2018)\n",
    "    elif model_name == 'LGB':\n",
    "        from lightgbm import LGBMClassifier\n",
    "        model = LGBMClassifier(boost='gbdt',\n",
    "                    num_leaves=135, \n",
    "                    scale_pos_weight=float(len(y_train)-np.sum(y_train.ravel()))/float(np.sum(y_train.ravel())),\n",
    "                    max_depth=-1,\n",
    "                    learning_rate=.04,\n",
    "                    max_bin=200,\n",
    "                    min_data_in_leaf= 60,\n",
    "                    objective='binary',\n",
    "                    metric='auc',\n",
    "                    num_threads=32,\n",
    "                    slient=False,\n",
    "                    num_boost_round =400)\n",
    "    else:\n",
    "        pass\n",
    "    return model\n",
    "\n",
    "def get_oof(clf,n_folds,x_train,y_train,x_test):\n",
    "    n_train = x_train.shape[0]\n",
    "    n_test = x_test.shape[0]\n",
    "    num_class = len(np.unique(y_train))\n",
    "    kf = KFold(n_splits=n_folds,random_state=2018)\n",
    "    oof_train = np.zeros((n_train,num_class-1))\n",
    "    oof_test = np.zeros((n_test,num_class-1))\n",
    "    \n",
    "    for i,(train_index,test_index)in enumerate(kf.split(x_train,y_train)):\n",
    "        print(i)\n",
    "        kf_x_train = x_train[train_index]\n",
    "        kf_y_train = y_train[train_index]\n",
    "        kf_x_test = x_train[test_index]\n",
    "        \n",
    "        clf.fit(kf_x_train,kf_y_train)\n",
    "        tmp_trafold = clf.predict_proba(kf_x_test)\n",
    "        tmp_test = clf.predict_proba(x_test)\n",
    "        if tmp_trafold.ndim != 1:\n",
    "            tmp_trafold = tmp_trafold[:,1:]\n",
    "            tmp_test = tmp_test[:,1:]\n",
    "        oof_train[test_index] = tmp_trafold\n",
    "        oof_test += tmp_test\n",
    "        \n",
    "    oof_test = oof_test/n_folds\n",
    "    return oof_train,oof_test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=36)]: Using backend ThreadingBackend with 36 concurrent workers.\n",
      "[Parallel(n_jobs=36)]: Done 128 tasks      | elapsed:    5.4s\n",
      "[Parallel(n_jobs=36)]: Done 378 tasks      | elapsed:   14.2s\n",
      "[Parallel(n_jobs=36)]: Done 728 tasks      | elapsed:   26.2s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "rfc_model = SelectModel('RFC')\n",
    "rfc_model.fit(x_train,y_train)\n",
    "joblib.dump(rfc_model,'rfc_model')\n",
    "\n",
    "lgb_model = SelectModel('LGB')\n",
    "lgb_model.fit(x_train,y_train)\n",
    "joblib.dump(lgb_model,'lgb_model')\n",
    "\n",
    "xgb_model = SelectModel('XGB')\n",
    "xgb_model.fit(x_train,y_train)\n",
    "joblib.dump(xgb_model,'xgb_model')\n",
    "\n",
    "gbc_model = SelectModel('GBC')\n",
    "gbc_model.fit(x_train,y_train)\n",
    "joblib.dump(gbc_model,'gbc_model')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_id = pd.read_csv('../../preprocess_data_new/valid_date.csv',usecols=['id']).values.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "rfc_model = joblib.load('rfc_model')\n",
    "lgb_model = joblib.load('lgb_model')\n",
    "xgb_model = joblib.load('xgb_model')\n",
    "gbc_model = joblib.load('gbc_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=36)]: Using backend ThreadingBackend with 36 concurrent workers.\n",
      "[Parallel(n_jobs=36)]: Done 128 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=36)]: Done 378 tasks      | elapsed:    0.3s\n",
      "[Parallel(n_jobs=36)]: Done 728 tasks      | elapsed:    0.6s\n",
      "[Parallel(n_jobs=36)]: Done 1178 tasks      | elapsed:    0.9s\n",
      "[Parallel(n_jobs=36)]: Done 1500 out of 1500 | elapsed:    1.1s finished\n"
     ]
    }
   ],
   "source": [
    "rfc_pred = rfc_model.predict_proba(x_test[:,:10800])[:,1]\n",
    "lgb_pred = lgb_model.predict_proba(x_test)[:,1]\n",
    "xgb_pred = xgb_model.predict_proba(x_test)[:,1]\n",
    "gbc_pred = gbc_model.predict_proba(x_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = pd.DataFrame()\n",
    "pred['id'] = test_id\n",
    "pred['prob'] = (rfc_pred + lgb_pred + xgb_pred + gbc_pred)/4\n",
    "import os\n",
    "if not os.path.exists('./pred'):\n",
    "    os.mkdir('pred')\n",
    "pred.to_csv('./pred/voting_pred.txt',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "xgb_pred.to_csv('./pred/voting_pred.txt',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc_res = pd.DataFrame()\n",
    "rfc_res['id'] = test_id\n",
    "rfc_res['prob'] = rfc_pred\n",
    "rfc_res.to_csv('./pred/rfc_res.txt',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "voting_pred AUC:0.8121\n",
    "rfc_res AUC:0.78029396528571"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_score = pd.Series(index=data.columns,data=rfc_model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sf5567    0.005345\n",
       "df5773    0.003776\n",
       "sf774     0.002786\n",
       "sf5322    0.002509\n",
       "sf3789    0.002225\n",
       "df5800    0.002219\n",
       "sf1930    0.002198\n",
       "sf6263    0.002123\n",
       "sf3890    0.002120\n",
       "sf2552    0.002108\n",
       "sf1554    0.002059\n",
       "sf6072    0.002029\n",
       "sf659     0.002025\n",
       "sf2668    0.002014\n",
       "sf6518    0.002003\n",
       "sf1228    0.001995\n",
       "sf2521    0.001975\n",
       "sf2145    0.001973\n",
       "sf3810    0.001955\n",
       "sf3267    0.001899\n",
       "sf46      0.001828\n",
       "sf3453    0.001703\n",
       "df1469    0.001689\n",
       "sf1561    0.001653\n",
       "sf4880    0.001606\n",
       "sf4968    0.001561\n",
       "sf3884    0.001554\n",
       "sf6437    0.001533\n",
       "sf3688    0.001502\n",
       "sf1276    0.001459\n",
       "            ...   \n",
       "df5223    0.000000\n",
       "df6256    0.000000\n",
       "df1785    0.000000\n",
       "df4222    0.000000\n",
       "df4223    0.000000\n",
       "mf2792    0.000000\n",
       "mf1582    0.000000\n",
       "mf2186    0.000000\n",
       "df1738    0.000000\n",
       "mf6468    0.000000\n",
       "mf6247    0.000000\n",
       "df1849    0.000000\n",
       "df6216    0.000000\n",
       "df1864    0.000000\n",
       "mf6052    0.000000\n",
       "df1992    0.000000\n",
       "df5197    0.000000\n",
       "mf6089    0.000000\n",
       "mf1646    0.000000\n",
       "mf2874    0.000000\n",
       "df1937    0.000000\n",
       "mf6138    0.000000\n",
       "mf2867    0.000000\n",
       "df5205    0.000000\n",
       "mf2852    0.000000\n",
       "df1922    0.000000\n",
       "df6249    0.000000\n",
       "mf6210    0.000000\n",
       "df1865    0.000000\n",
       "df1940    0.000000\n",
       "Length: 11334, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_score.sort_values(ascending=False) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 神经网络效果很差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 23425 samples, validate on 10040 samples\n",
      "Epoch 1/10\n",
      "23425/23425 [==============================] - 2s 106us/step - loss: 14.7869 - acc: 0.0725 - val_loss: 14.7183 - val_acc: 0.0768\n",
      "Epoch 2/10\n",
      "23425/23425 [==============================] - 2s 100us/step - loss: 14.7869 - acc: 0.0725 - val_loss: 14.7183 - val_acc: 0.0768\n",
      "Epoch 3/10\n",
      "23425/23425 [==============================] - 2s 106us/step - loss: 14.7869 - acc: 0.0725 - val_loss: 14.7183 - val_acc: 0.0768\n",
      "Epoch 4/10\n",
      "23425/23425 [==============================] - 2s 89us/step - loss: 14.7869 - acc: 0.0725 - val_loss: 14.7183 - val_acc: 0.0768\n",
      "Epoch 5/10\n",
      "23425/23425 [==============================] - 3s 117us/step - loss: 14.7869 - acc: 0.0725 - val_loss: 14.7183 - val_acc: 0.0768\n",
      "Epoch 6/10\n",
      "23425/23425 [==============================] - 2s 89us/step - loss: 14.7869 - acc: 0.0725 - val_loss: 14.7183 - val_acc: 0.0768\n",
      "Epoch 7/10\n",
      "23425/23425 [==============================] - 2s 92us/step - loss: 14.7869 - acc: 0.0725 - val_loss: 14.7183 - val_acc: 0.0768\n",
      "Epoch 8/10\n",
      "23425/23425 [==============================] - 3s 108us/step - loss: 14.7869 - acc: 0.0725 - val_loss: 14.7183 - val_acc: 0.0768\n",
      "Epoch 9/10\n",
      "23425/23425 [==============================] - 2s 90us/step - loss: 14.7869 - acc: 0.0725 - val_loss: 14.7183 - val_acc: 0.0768\n",
      "Epoch 10/10\n",
      "23425/23425 [==============================] - 3s 107us/step - loss: 14.7869 - acc: 0.0725 - val_loss: 14.7183 - val_acc: 0.0768\n"
     ]
    }
   ],
   "source": [
    "from keras import models \n",
    "from keras import layers \n",
    "def build_model(): \n",
    "    model = models.Sequential() \n",
    "    model.add(layers.Dense(50,activation='relu',input_shape=(11334,))) \n",
    "    model.add(layers.Dense(20,activation='relu')) \n",
    "    model.add(layers.Dense(1,activation='sigmoid'))\n",
    "    \n",
    "    model.compile(optimizer='rmsprop',# 还可以通过optimizer = optimizers.RMSprop(lr=0.001)来为优化器指定参数 \n",
    "                  loss='binary_crossentropy', # 等价于loss = losses.binary_crossentropy \n",
    "                  metrics=['accuracy'] # 等价于metrics = [metircs.binary_accuracy]\n",
    "                  )  \n",
    "    return model \n",
    "model = build_model()\n",
    "history = model.fit(x_train,y_train,epochs=10,batch_size=1024,class_weight = 'auto',validation_data=(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict_proba(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.49732174296011206"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 评价指标 AUC\n",
    "from sklearn import metrics\n",
    "metrics.roc_auc_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.6/site-packages/lightgbm/engine.py:102: UserWarning: Found `num_boost_round` in params. Will use it instead of argument\n",
      "  warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.6/site-packages/lightgbm/engine.py:102: UserWarning: Found `num_boost_round` in params. Will use it instead of argument\n",
      "  warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.6/site-packages/lightgbm/engine.py:102: UserWarning: Found `num_boost_round` in params. Will use it instead of argument\n",
      "  warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.6/site-packages/lightgbm/engine.py:102: UserWarning: Found `num_boost_round` in params. Will use it instead of argument\n",
      "  warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.6/site-packages/lightgbm/engine.py:102: UserWarning: Found `num_boost_round` in params. Will use it instead of argument\n",
      "  warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 12h 51min 2s, sys: 41min 56s, total: 13h 32min 59s\n",
      "Wall time: 33min 53s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# 第一级\n",
    "model_list = ['XGB','RFC','LGB']\n",
    "new_train_list = []\n",
    "new_test_list = []\n",
    "for model in model_list:\n",
    "    clf = SelectModel(model)\n",
    "    oof_train,oof_test = get_oof(clf,5,x_train,y_train,x_test)\n",
    "#     oof_train,oof_test = get_oof(clf,5,x_resampled,y_resampled,x_test)\n",
    "    new_train_list.append(oof_train)\n",
    "    new_test_list.append(oof_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_train = np.hstack(tuple(new_train_list))\n",
    "new_test = np.hstack(tuple(new_test_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda3/lib/python3.6/site-packages/lightgbm/engine.py:102: UserWarning: Found `num_boost_round` in params. Will use it instead of argument\n",
      "  warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n",
      "[Parallel(n_jobs=36)]: Done 128 tasks      | elapsed:    0.9s\n",
      "[Parallel(n_jobs=36)]: Done 378 tasks      | elapsed:    1.7s\n",
      "[Parallel(n_jobs=36)]: Done 728 tasks      | elapsed:    2.5s\n",
      "[Parallel(n_jobs=36)]: Done 800 out of 800 | elapsed:    2.7s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Iter       Train Loss      OOB Improve   Remaining Time \n",
      "         1           0.4718           0.0134           21.06s\n",
      "         2           0.4568           0.0100           13.08s\n",
      "         3           0.4514           0.0083           10.43s\n",
      "         4           0.4447           0.0073            9.12s\n",
      "         5           0.4372           0.0061            8.32s\n",
      "         6           0.4321           0.0059            7.83s\n",
      "         7           0.4310           0.0040            7.45s\n",
      "         8           0.4205           0.0042            7.13s\n",
      "         9           0.4190           0.0036            6.90s\n",
      "        10           0.4175           0.0030            6.70s\n",
      "        20           0.3944           0.0012            5.66s\n",
      "        30           0.3844           0.0006            5.19s\n",
      "        40           0.3831           0.0003            4.88s\n",
      "        50           0.3799           0.0002            4.62s\n",
      "        60           0.3773          -0.0000            4.38s\n",
      "        70           0.3711          -0.0001            4.15s\n",
      "        80           0.3762           0.0000            3.93s\n",
      "        90           0.3751          -0.0000            3.74s\n",
      "       100           0.3739          -0.0000            3.53s\n",
      "       200           0.3628          -0.0000            1.81s\n",
      "       300           0.3567           0.0000            0.00s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight='balanced', dual=False,\n",
       "          fit_intercept=True, intercept_scaling=1, max_iter=150,\n",
       "          multi_class='ovr', n_jobs=1, penalty='l2', random_state=2018,\n",
       "          solver='liblinear', tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 第二级\n",
    "from lightgbm import LGBMClassifier\n",
    "lgb_model = LGBMClassifier(boost='gbdt',\n",
    "            num_leaves=145, \n",
    "            scale_pos_weight=float(len(y_train)-np.sum(y_train.ravel()))/float(np.sum(y_train.ravel())),\n",
    "            max_depth=3,\n",
    "            learning_rate=.01,\n",
    "            max_bin=200,\n",
    "            min_data_in_leaf= 3,\n",
    "            objective='binary',\n",
    "            metric='auc',\n",
    "            num_threads=32,\n",
    "            slient=False,\n",
    "            num_boost_round =120)\n",
    "lgb_model.fit(new_train,y_train)\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc_model = RandomForestClassifier(n_estimators=800,\n",
    "                               n_jobs =36,\n",
    "                               criterion='gini',\n",
    "                               max_depth=5,\n",
    "                               min_samples_split=8,\n",
    "                               max_features='sqrt',\n",
    "                               class_weight='balanced',\n",
    "                               verbose =1,\n",
    "                               random_state=2018)\n",
    "rfc_model.fit(new_train,y_train)\n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "xgb_model = XGBClassifier(max_depth=3,\n",
    "                      learning_rate =0.02, \n",
    "                      booster='gbtree',\n",
    "                      objective='binary:logistic',\n",
    "                      early_stopping_rounds=100,\n",
    "                      scale_pos_weight=float(len(y_train)-np.sum(y_train))/float(np.sum(y_train)),\n",
    "                      eval_metric='auc',\n",
    "                      gamma=0.1,\n",
    "                      reg_lambda=1,\n",
    "                      subsample=0.9,\n",
    "                      min_child_weight=1,\n",
    "                      seed=2018,\n",
    "                      silent=False,\n",
    "                      n_jobs=24,\n",
    "                      num_boost_round =250\n",
    "                     )\n",
    "xgb_model.fit(new_train,y_train)\n",
    "\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "gbc_model = GradientBoostingClassifier(loss='deviance',\n",
    "                                   learning_rate =0.05,\n",
    "                                   n_estimators=300,\n",
    "                                   subsample=0.9,\n",
    "                                   max_depth=3,\n",
    "                                   verbose=1,\n",
    "                                  random_state=2018)\n",
    "gbc_model.fit(new_train,y_train)\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lg_model = LogisticRegression(penalty='l2',\n",
    "                        class_weight='balanced',\n",
    "                        random_state=2018,\n",
    "                        solver='liblinear',\n",
    "                        max_iter=150)\n",
    "lg_model.fit(new_train,y_train)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y_pred = lgb_model.predict_proba(new_test)[:,1] + \\\n",
    "#          rfc_model.predict_proba(new_test)[:,1] + \\\n",
    "#          lgb_model.predict_proba(new_test)[:,1] + \\\n",
    "#          3*gbc_model.predict_proba(new_test)[:,1] + \\\n",
    "#          lg_model.predict_proba(new_test)[:,1]\n",
    "y_pred = lg_model.predict_proba(new_test)[:,1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "stack_pred = pd.DataFrame({'id':test_id})\n",
    "stack_pred['prob'] = y_pred\n",
    "stack_pred.to_csv('stack_pred_lg.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "线上分数：  \n",
    "stack_pred AUC:0.8297  \n",
    "stack_pred1 AUC: 0.82757885054343"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 测试over sampling效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=36)]: Done 128 tasks      | elapsed:    1.5s\n",
      "[Parallel(n_jobs=36)]: Done 378 tasks      | elapsed:    4.1s\n",
      "[Parallel(n_jobs=36)]: Done 728 tasks      | elapsed:    7.7s\n",
      "[Parallel(n_jobs=36)]: Done 1000 out of 1000 | elapsed:   10.4s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight='balanced',\n",
       "            criterion='gini', max_depth=5, max_features='sqrt',\n",
       "            max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "            min_impurity_split=None, min_samples_leaf=1,\n",
       "            min_samples_split=8, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=1000, n_jobs=36, oob_score=False,\n",
       "            random_state=2018, verbose=1, warm_start=False)"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc_model = RandomForestClassifier(n_estimators=1000,\n",
    "                               n_jobs =36,\n",
    "                               criterion='gini',\n",
    "                               max_depth=7,\n",
    "                               min_samples_split=8,\n",
    "                               max_features='sqrt',\n",
    "                               class_weight='balanced',\n",
    "                               verbose =1,\n",
    "                               random_state=2018)\n",
    "rfc_model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=36)]: Done 128 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=36)]: Done 378 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=36)]: Done 728 tasks      | elapsed:    0.4s\n",
      "[Parallel(n_jobs=36)]: Done 1000 out of 1000 | elapsed:    0.6s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7669968586030466"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rfc_model.predict_proba(x_test)[:,1]# 评价指标 AUC\n",
    "from sklearn import metrics\n",
    "metrics.roc_auc_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "over sampling AUC: 0.7483107140570182  \n",
    "raw  AUC: 0.774660626396733"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21924.0"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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